Monitoring Recessions: A Bayesian Sequential Quickest Detection Method

Monitoring business cycles faces two potentially conflicting objectives: accuracy and timeliness. To strike a balance between the dual objectives, we develop a Bayesian sequential quickest detection method to identify turning points in real time and propose a sequential stopping time as a solution. Using four monthly indexes of real economic activity in the US, we evaluate the method's real-time ability to date the past five recessions. The proposed method identifies similar turning point dates as the National Bureau of Economic Research (NBER), with no false alarms, but on average dates peaks 4 months faster and troughs 10 months faster relative to the NBER announcement. The timeliness of our method is also notable compared to the dynamic factor Markov-switching model - the average lead time is about 5 months in dating peaks and 2 months in dating troughs.